What is Machine Learning?
Understand how computers learn patterns from historical data and use those patterns to make predictions about new situations.
What you will learn
30-second explanation
Machine Learning allows software to discover patterns from examples instead of requiring humans to write every rule manually.
A model studies historical data, learns relationships inside that data, and applies those relationships when it receives new information.
Understand
Traditional programming vs Machine Learning
The key difference is how the system obtains its rules.
Traditional software
A developer writes explicit rules that tell the software exactly how to respond.
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Output
Example: If the order value exceeds €50, apply free delivery.
Machine Learning
The system examines examples and learns useful rules or relationships from the data.
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Learned model
Example: Learn which transactions are likely fraudulent based on previous fraud cases.
Visualize
How Machine Learning works
Most Machine Learning projects follow a repeating lifecycle rather than ending after the first model is trained.
Collect
Gather historical examples related to the problem you want to solve.
Prepare
Clean the data, handle missing values, and select useful information.
Train
Allow an algorithm to discover patterns from the training examples.
Evaluate
Test how accurately the model performs on data it has not seen before.
Predict
Use the trained model to make predictions on new real-world inputs.
Simple example
Predicting the price of a house
Suppose you want to estimate the selling price of a property. You provide the model with historical house-sale data.
Input features
Location, floor area, property age, number of rooms, and energy rating.
Known answer
The actual selling price of every historical property.
Learned patterns
The model discovers how different property features affect price.
New prediction
It estimates the value of a property it has never seen before.
The model does not understand houses like a human.
It finds mathematical relationships between the available inputs and previous selling prices. Its result is only as reliable as its data and evaluation process.
Explore
Three main types of Machine Learning
1Supervised learning+
The model learns from examples where the correct answer is already known.
Example
Train on previous emails labelled as spam or not spam, then classify new emails.
2Unsupervised learning+
The model explores data without predefined answers and discovers hidden structures or groups.
Example
Analyse customer behaviour and automatically group customers with similar purchasing patterns.
3Reinforcement learning+
The system learns by taking actions and receiving rewards or penalties based on the outcome.
Example
Train a robot to navigate a room by rewarding successful movement and penalising collisions.
Use
Where Machine Learning is used
Machine Learning is most useful when historical data contains patterns that can help predict or classify future situations.
Fraud detection
Banks analyse transaction patterns to identify unusual or potentially fraudulent activity.
Recommendations
Streaming and shopping platforms predict which products, videos, or songs a person may prefer.
Predictive maintenance
Factories analyse machine sensor data to predict failures before equipment stops working.
Medical support
Models help identify patterns in scans, laboratory results, and patient histories.
Spam filtering
Email systems classify incoming messages based on patterns learned from previous examples.
Demand forecasting
Businesses predict future product demand using historical sales and market information.
Compare
AI, Machine Learning, and Deep Learning
These terms are related, but they do not mean exactly the same thing.
Artificial Intelligence
The broad field of building machines that perform tasks associated with intelligence.
Machine Learning
A branch of AI in which systems learn patterns from data.
Deep Learning
A specialised area of Machine Learning based on multi-layer neural networks.
Deep Learning is part of Machine Learning, and Machine Learning is part of Artificial Intelligence.
Consider Machine Learning when
- ✓ You have enough relevant historical data.
- ✓ The required rules are difficult to define manually.
- ✓ The problem involves prediction or classification.
- ✓ Patterns may change as new data becomes available.
- ✓ Predictions create measurable business value.
A normal rule may be better when
- • The business logic is simple and fully known.
- • Exact and predictable behaviour is required.
- • Very little usable data is available.
- • A mistake would create unacceptable risk.
- • The cost of training and monitoring exceeds the benefit.
Avoid
Why Machine Learning projects fail
Selecting an algorithm is only one small part of delivering a reliable Machine Learning system.
Poor-quality data
Incomplete, incorrect, or outdated training data produces unreliable models.
Biased examples
A model may repeat or amplify unfair patterns that already exist in its training data.
Overfitting
The model memorises its training examples but performs poorly on new situations.
Wrong success metric
A model can look successful statistically while still failing the actual business objective.
Data drift
Real-world behaviour changes over time, making an older model less accurate.
No human oversight
High-impact decisions should not rely blindly on predictions without review and controls.
Key takeaway
Machine Learning converts historical examples into a model that can make useful predictions.
The algorithm matters, but successful Machine Learning depends just as much on problem selection, data quality, evaluation, deployment, monitoring, and responsible human oversight.
Remember the basic pattern:
Learn from previous examples → validate on unseen data → use the model carefully on new situations.
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